docs: def efron_endpoints_for_abc_confidence_interval(conf_percentage,
                                                  model_obj,
                                                  init_vals,
                                                  bias_correction,
                                                  acceleration,
                                                  std_error,
                                                  empirical_influence,
                                                  **fit_kwargs):
      """
      Calculates the endpoints of the equal-tailed, `conf_percentage`%
      approximate bootstrap confidence (ABC) interval.
  
      Parameters
      ----------
      conf_percentage : scalar in the interval (0.0, 100.0).
          Denotes the confidence-level for the returned endpoints. For instance,
          to calculate a 95% confidence interval, pass `95`.
      model_obj : an instance or sublcass of the MNDC class.
          Should be the model object that corresponds to the model we are
          constructing the bootstrap confidence intervals for.
      init_vals : 1D ndarray.
          The initial values used to estimate the desired choice model.
      bias_correction : 1D ndarray of scalars.
          Contains the computed bias correction for the MLE estimates that the
          ABC interval is being computed for.
      acceleration : 1D ndarray of scalars.
          Should contain the ABC intervals' estimated acceleration constants.
      std_error : 1D ndarray.
          Contains the standard error of the MLE estimates for use in the ABC
          confidence intervals.
      empirical_influence : 2D ndarray.
          Should have one row for each observation. Should have one column for
          each parameter in the parameter vector being estimated. Elements should
          denote the empirical influence of the associated observation on the
          associated parameter.
      fit_kwargs : additional keyword arguments, optional.
          Should contain any additional kwargs used to alter the default behavior
          of `model_obj.fit_mle` and thereby enforce conformity with how the MLE
          was obtained. Will be passed directly to `model_obj.fit_mle`.
  
      Returns
      -------
      lower_endpoint, upper_endpoint : 1D ndarray.
          Contains the lower or upper endpoint, respectively, from our
          `conf_percentage`% ABC interval.
  
      References
      ----------
      Efron, Bradley, and Robert J. Tibshirani. An Introduction to the Bootstrap.
          CRC press, 1994. Section 22.6.
  
      Notes
      -----
      This function does not directly implement Equation 22.33. Instead, it
      implements Efron's endpoint calculations from 'abcnon.R' in the 'bootstrap'
      library in R. It is not clear where these calculations come from, and
      if/how these calculations are equivalent to Equation 22.33.
      """
      # Calculate the percentiles for the lower and upper endpoints
      alpha_percent = get_alpha_from_conf_percentage(conf_percentage)
      # Note that we divide by 100 because scipy.stats.norm.ppf only accepts
      # floats between 0.0 and 1.0
      lower_percentile = alpha_percent / 2.0
      upper_percentile = 100 - lower_percentile
      # Calculate the lower endpoint
      lower_endpoint = efron_endpoint_from_percentile_abc(model_obj,
                                                          init_vals,
                                                          lower_percentile,
                                                          bias_correction,
                                                          acceleration,
                                                          std_error,
                                                          empirical_influence,
                                                          **fit_kwargs)
      # Calculate the upper endpoint
      upper_endpoint = efron_endpoint_from_percentile_abc(model_obj,
                                                          init_vals,
                                                          upper_percentile,
                                                          bias_correction,
                                                          acceleration,
                                                          std_error,
                                                          empirical_influence,
                                                          **fit_kwargs)
      return lower_endpoint, upper_endpoint